1. Field of the Invention
The present invention generally relates to computer implemented planning resources and decision support tools and, more particularly, to a tool in which core production planning information is provided to a solver which generates a best can do (BCD) match between assets and demands. The invention generates a BCD match between existing assets and demands across multiple manufacturing facilities within boundaries established by manufacturing specifications and process flows and business policies to determine which demands can be met in what time frame by microelectronics (wafer to card) or related (for example disk drives) manufacturing and establishes a set of actions or guidelines for manufacturing to incorporate into their Manufacturing Execution System to insure the delivery commitments are met in a timely fashion.
2. Background Description
Within the complexity of microelectronics and related manufacturing, four related decision areas or tiers can be distinguished based on the time scale of the planning horizon and the apparent width of the opportunity window. To facilitate an understanding of the four decision tiers in semiconductor manufacturing, consider the following oven example, with reference to FIG. 1 which is a diagram associated with this example.
Within a zone of control 10, there is a coater machine 12, a work-in-progress (WIP) queue 14, and an oven set 16. Wafers move around the zone of control in groups of twenty-five, called a lot. All wafers in the lot are the same type. Each lot must pass through the oven operation ten times. Each oven set is composed of four ovens or tubes 161, 162, 163, and 164 and one robot 166 to load and unload the oven. It takes about ten minutes to load or unload an oven. The process time in the oven depends on the iteration. We will assume one lot to an oven at a time. Before a wafer enters into the oven, it must be coated by the coater machine 12. The coating process takes twenty minutes. The coating expires in four hours. If the coating expires, the wafer must be stripped, cleaned, and then recoated. This process takes four hours and often generates yield losses.
The first decision tier, strategic scheduling, is driven by the time frame or lead time required for the business plan, resource acquisition, and new product introduction. This tier can often be viewed in two parts; very long-term and long-term. Here, decision makers are concerned with a set of problems that are three months to seven years into the future. Issues considered include, but are not limited to, what markets they will be in, general availability of tooling and workers, major changes in processes, changes in or risk assessment of demand for existing product, required or expected incremental improvements in the production process, lead times for additional tooling, manpower and planning. In the oven example of FIG. 1, very-long-term decisions are made on whether the ovens are necessary to the production process, and if so the characteristics needed in the ovens. Long-term decisions are made about how many ovens to buy. Tools typically used in planning of this scope are models for capacity planning, cost/pricing, investment optimization, and simulations of key business measures.
The second tier, tactical scheduling, deals with problems the company faces in the next week to six months. Estimates are made of yields, cycle times, and binning percentages. Permissible substitutions are identified. Decisions are made about scheduling starts or releases into the manufacturing line (committing available capacity to new starts). Delivery dates are estimated for firm orders, available "outs" by time buckets are estimated for bulk products, and daily going rates for schedule driven product are set. The order/release plan is generated/regenerated. Reschedules are negotiated with or requested by the ultimate customer. In the oven example of FIG. 1, typical decision areas would include the daily going rate for different products, the allocation of resources between operations, the number of operators to assign, and machine dedication. Tools typically used in the planning and scheduling of this phase are forward schedulers, fast capacity checkers, and optimization of capacity, commits and cost.
The third tier, operational scheduling, deals with the execution and achievement of a weekly plan. Shipments are made. Serviceability levels are measured. Recovery actions are taken. Optimized consumption of capacity and output of product computed. Tools typically used in support of daily activities are decision support, recovery models, prioritization techniques and deterministic forward schedulers. Manufacturing Execution Systems (MES) are used for floor communications and control. In the oven example of FIG. 1, priorities would be placed on each lot arriving at the ovens based on their relevance to the current plan or record. If the ovens "go down", their priority in the repair queue would be set by decisions made in this tier.
The fourth tier, dispatch scheduling or response system, addresses the problems of the next hour to a few weeks by responding to conditions as they emerge in real time and accommodate variances from availability assumed by systems in the plan creation and commitment phases. Essentially, they instruct the operator what to do next to achieve the current goals of manufacturing. Dispatch scheduling decisions concern monitoring and controlling of the actual manufacturing flow or logistics. Here, decisions are made concerning trade-offs between running test lots for a change in an existing product or a new product and running regular manufacturing lots, lot expiration, prioritizing late lots, positioning preventive maintenance downtime, production of similar products to reduce setup time, downstream needs, simultaneous requests on the same piece of equipment, preferred machines for yield considerations, assigning personnel to machines, covering for absences, and reestablishing steady production flow after a machine has been down. In the oven example, the question should be which lot (if any) should be run next when an oven is free. Tools used are rule based dispatchers, short interval schedulers and mechanical work-in-progress (WIP) limiting constructions.
Of course, there is overlap and interaction between the four decision tiers, but typically different groups are responsible for different scheduling decisions. For example, maintenance may decide on training for their personnel, on work schedules for their people, preventive maintenance, and which machine to repair next. Finance and each building superintendent may make decisions on capital equipment purchases. Industrial Engineering may have the final say on total manpower, but a building superintendent may do the day-to-day scheduling. Marketing may decide when orders for products can be filled and what schedule commitments to make. For strategic and operational decisions, these groups and their associated decision support tools are loosely coordinated or coupled. Finance only requires an estimate of required new tools from each building to estimate capital purchase. Each building requires an estimate on new tool requirements from the product development people. Dispatch decisions must be tightly coupled. Lots only get processed when the appropriate tool, operator, and raw material are available. At dispatch rough estimates are no longer sufficient. If a machine is down maintenance must have the appropriately trained individual available to repair the machine. Manufacturing must have the appropriate mix of tools and workers to produce finished goods on a timely basis. At dispatch the decisions made by various groups must be in synchronization or nothing is produced. A manufacturing facility accommodates this tight coupling in only one of two ways; slack (extra tooling and manpower, long lead times, limited product variation, excess inventory and people, differential quality, brand loyalty, and so forth) or strong information systems to make effective decisions.
Within the second and third decision tiers, a major planning activity undertaken by microelectronic firms is matching assets with demands. This activity can be broken into three major types of matching that are used throughout Microelectronics to support decision making:
(a) Materials Requirements Planning (MRP) type of matching-- "Opportunity Identification" or "Wish list". For a given set of demand and a given asset profile what work needs to be accomplished to meet the demand. PA1 (b) Projected or Estimated Supply Plan (PSP/ESP). Given a set of assets, manufacturing specifications, and business guidelines this application creates an expected or projected supply picture over the next "t" time units. The user supplied guidelines to direct how to flow or flush assets "forward" to some inventory or holding point. PA1 (c) Best Can Do (BCD). Given the current manufacturing condition and a prioritized set of demands which demands can be met in what time frame. BCD generally refers to a large set of demands.
Arguably, the oldest type of matching is Material Requirements Planning (MRP). MRP is a system for translating demand for final products into specific raw material and manufacturing activity requirements by exploding demand backwards through the bill of material (BOM) and assets. Many authors have published papers and books on MRP. For example, Joseph Orlickly wrote Material Requirements Planning, published by McGraw-Hill, which has become a standard reference. As practiced in the microelectronics industry, MRP systems operate at a specific part number and inventory holding point level of detail.
The difficulty with traditional MRP is it does not provide an estimate about which demand will be met if insufficient resources are available and secondly how to prioritize manufacturing activity in light of insufficient resources. Therefore, it does not directly meet the business requirement filled by BCD. To overcome these limitations and answer the core BCD question, support tools were developed and added to MRP to examine the output of the MRP solution to help the user identify resource constraints, shift schedules to better utilize resources, and make limited suggestions on how to alter demand. These systems are often called extended MRP or MRP II. Historically, these tools have not evolved beyond the what-if stage and require much human intervention to create an intelligent feasible plan. The size and complexity the BCD question in semiconductor and microelectronics manufacturing have made the "add on" tools for MRP very limited in their effectiveness.
Additionally, traditional MRP logic does not adequately handle binned parts. Multiple integrated circuit chips, such as microprocessors, are manufactured on a single semiconductor wafer and separated into individual chips by dicing the wafer. Although all the chips are manufactured on a single wafer, testing each chip will reveal that there are variances in the performances of the chips. Assume that the microprocessors are designed for a particular clock speed, say 200 MHz. However, testing the chips shows that there is only a 50% yield of chips meeting this criteria. Of the remaining 50% of the chips, some may perform at a slower but still fast clock speed, say 175 MHz, and others at a still slower clock speed, say 150 Mhz. Traditional MRP vastly over state the required wafer starts needed to meet demand in binning situations. However, for some time an optimization model has been known which minimizes wafer starts in binning situations. Traditionally, this optimization was used in a stand alone form or within another optimization routine. The binning optimization model has not been used within an MRP designed to support BCD.
Within the semiconductor industry, a type of matching called projected supply planning was developed to partially meet the BCD business requirement. This matching uses aggregated production specification information and simple algorithms to generate a feasible supply plan. Again, this matching has clear limitations in its effectiveness to meet the BCD requirement.
During the past ten years, a variety of attempts have been made to apply Linear Programming (LP) decision technology in stand alone mode to directly support the BCD requirement in the semiconductor and microelectronics industry. This work suffered two major deficiencies. First, it operated as a black box assigning due dates to demands disconnected from the MRP approach, and therefore it failed to provide users an easy path to understand what was needed to meet demands. Since the parameters such as cycle times, yields, due dates for receipts, etc. are estimates, a good planner can identify places where manufacturing performance can be improved if required to meet important demands. Second, the LP structures failed to deal with critical decision trade-offs (such as demand class) common in the semiconductor industry and forced it to solve only subsets of the BCD matching question.
During the past five years, a few simple heuristic approaches have been developed to replace the LP based solver in generating a BCD black box answer. These approaches suffered the same deficiencies as the LP solution in failing to be connected to the MRP approach and simplifying the model structure relative to the complex flows in semiconductor manufacturing.
Additionally, most providers of the direct black box BCD tools either provided an LP approach or a heuristic approach. A few provided access to both, but in a disconnected manner. They did not attempt to harness the powerful synergy between the two.